The ML Platform is a critical component in increasing revenue at our firm. The role will
require everything from high-level architecture design to performant implementation.
Responsibilities include :
- Build distributed systems supporting complex modeling over time series data
- Lead design, development, and deployment of machine learning systems
- Understand and translate modeling goals & requirements into an engineering roadmap; future responsibilities could include leading key engineering initiatives in the forecasting platform
- Work closely with business side
- Review code developed by other developers and provide feedback to ensure best
practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency)
Qualifications
- Industry experience with modern ML infrastructure, ML libraries, and ML ops
- Have built large scale machine learning models and systems
- Familiarity with the fundamentals of probability,
- Master's degree in Computer Science, Data Science, Machine Learning, or a related field. Ph.D. is a plus
- Fluency in Python with the ability to write clean, modular, well-documented code as well as a solid understanding of coding best practices
- 5+ years in industry developing and deploying machine learning models, preferably in NLP, LLM and ideally generative AI domains
- Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or Transformers
- Familiarity with cloud platforms (AWS) and containerization technologies (Docker)
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